This information is important for the designs of new extracellular biomaterial that can better mimic the biological environment, and improve clinical outcomes of musculoskeletal tissue degenerations and associated disorders.Cartilage viscoelasticity changes as cartilage degenerates. Hence, a cartilage viscoelasticity measurement could be an alternative to traditional imaging methods for osteoarthritis diagnosis. In a previous study, we confirmed the feasibility of viscoelasticity measurement in ex vivo bovine cartilage using the Lamb wave method. https://www.selleckchem.com/products/odm-201.html However, the wave speed-frequency curve of Lamb wave is totally nonlinear and the cartilage thickness could significantly affect the Lamb wave speed, making wave speed measurements and viscoelasticity inversion difficult. The objective of this study was to measure the cartilage viscoelasticity using the Rayleigh wave method (RWM). Rayleigh wave speed in the ex vivo bovine cartilage was measured, and exists only in the near-source and far-field region. The estimated cartilage elasticity was 0.66 ± 0.05 and 0.59 ± 0.07 MPa for samples 1 and 2, respectively; the estimated cartilage viscosity was 24.2 ± 0.7 and 27.1 ± 1.8 Pa?s for samples 1 and 2, respectively. These results were found to be highly reproducible, validating the feasibility of viscoelasticity measurement in ex vivo cartilage using the RWM. Current method of cartilage viscoelasticity measurement might be translated into in vivo application.The transition of the inflow jet to turbulence is crucial in understanding the pathology of brain aneurysms. Previous works Le et al. (2010, 2013) have shown evidence for a highly dynamic inflow jet in the ostium of brain aneurysms. While it is highly desired to investigate this inflow jet dynamics in clinical practice, the constraints on spatial and temporal resolutions of in vivo data do not allow a detailed analysis of this transition. In this work, Dynamic Mode Decomposition (DMD) is used to identify the most energetic modes of the inflow jet in patient-specific models of internal carotid aneurysms via the utilization of high-resolution simulation data. It is hypothesized that dynamic modes are not solely controlled by the blood flow waveform at the parent artery. They are also dependent on jet-wall interaction phenomena. DMD analysis shows that the spatial extent of low- frequency modes corresponds well to the most energetic areas of the inflow jet. The high-frequency modes are short-lived and correspond to the flow separation at the proximal neck and the jet's impingement onto the aneurysmal wall. Low-frequency modes can be reconstructed at relatively low spatial and temporal resolutions comparable to ones of in vivo data. The current results suggest that DMD can be practically useful in analyzing blood flow patterns of brain aneurysms with in vivo data.The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. We trained deep neural networks on a large set of synthetic inertial data derived from a clinical marker-based motion-tracking database of hundreds of subjects. We used data augmentation techniques and an automated calibration approach to reduce error due to variability in sensor placement and limb alignment. On left-out subjects, lower extremity kinematics could be predicted with a mean (±STD) root mean squared error of less than 1.27° (±0.38°) in flexion/extension, less than 2.52° (±0.98°) in ad/abduction, and less than 3.34° (±1.02°) internal/external rotation, across walking and running trials. Errors decreased exponentially with the amount of training data, confirming the need for large datasets when training deep neural networks. While this framework remains to be validated with true inertial measurement unit data, the results presented here are a promising advance toward convenient estimation of gait kinematics in natural environments. Progress in this direction could enable large-scale studies and offer new perspective into disease progression, patient recovery, and sports biomechanics.To evaluate intrafractional head motion (IFM) in patients who underwent intracranial stereotactic radiosurgery with the ExacTrac X-ray system (ETX) and a frameless mask.
A total of 143 patients who completed a pre-treatment examination for IFM were eligible for this study. The frameless mask type B R408 (Klarity Medical &amp; Equipment Co., Ltd., Guangzhou, China), which covers the back of the head, and the entire face, was used for patient immobilization. After the initial 6D correction and first X-ray verification (IFM), X-ray verification was performed every 3?min for a duration of 15?min. The IFM(2???p???6) was calculated as the positional difference from IFM. In addition, the inter-phase IFM (IP-IFM) and IFMwere calculated. The IP-IFM was defined as IFM?-?IFM, and IFMas the difference between the values after all patients were asked to move their heads intentionally with the frameless mask on.
Both translational IFMand IP-IFM exceeded 1?mm for a single patient, whereas, for all patients, the translational IFMvalues were kept to within 1?mm in all directions. The proportions of the rotational IFM, IP-IFM, and IFMvalues within 0.5° were greater than 94.4%, 98.6%, and 90.2% for all of the rotational axes, respectively.
A frameless mask achieved highly accurate patient positioning in combination with ETX and a 6°-of-freedom robotic couch; however, a deviation over 1?mm and 0.5° was observed with low frequency. Therefore, X-ray verification and correction are required during treatment.
A frameless mask achieved highly accurate patient positioning in combination with ETX and a 6°-of-freedom robotic couch; however, a deviation over 1?mm and 0.5° was observed with low frequency. Therefore, X-ray verification and correction are required during treatment.